SEARCH

SEARCH BY CITATION

Keywords:

  • Aquatic insects;
  • environmental heterogeneity;
  • idiosyncratic species;
  • Mediterranean temporary ponds;
  • nestedness;
  • pond conservation;
  • spatial descriptors;
  • species replacement;
  • turnover

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Appendices
  1. Macroinvertebrate assemblages of temporary ponds are ideal model systems to explore biodiversity patterns and metacommunity ecology. In addition, the study of the environmental variables driving such biodiversity patterns is essential in establishing proper guidelines for the conservation of the singular fauna of temporary ponds, especially since such ponds are vulnerable systems.
  2. We analysed the macroinvertebrate assemblages and environmental characteristics of 80 ponds spread across the Doñana National Park, SW Spain to (i) analyse macroinvertebrate β-diversity and metacommunity structure; and (ii) discern the main environmental and spatial drivers of these patterns.
  3. The pond network was highly heterogeneous as temporary ponds were highly variable. Macroinvertebrate β-diversity partitioning showed that species replacement made the greatest contribution to total β-diversity while the contribution of nestedness was small. The macroinvertebrate community structure and β-diversity were similarly driven by: electrical conductivity (and co-variables alkalinity, pH, and ion concentrations), plant richness (and the co-variable pond surface area), maximum depth, marsh, and coastal proximity as well as two spatial descriptors extracted from Moran's eigenvector maps. The spatial descriptors indicated that large interpond distances were involved, suggesting that species dispersal limitations only take place over long distances in the area.
  4. Those taxa that departed from the general nested pattern, termed idiosyncratic, significantly contributed to the maintenance of high pond network diversity through the species replacement and occurred within particular environmental conditions in the pond network.
  5. These results reveal that environmental heterogeneity and connectivity are key factors in the preservation of high macroinvertebrate diversity in nested pond networks with high numbers of idiosyncratic species.

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Appendices

The metacommunity is an emergent concept that considers the impact of the exchange of species in heterogeneous environments (Leibold et al., 2004; Urban & Skelly, 2006). Temporary ponds, which are characterised by annual inundation-desiccation cycles (Williams, 1997), are ideal model systems to study metacommunity ecology given their simple structure, local abundance, and occurrence in pond networks that demonstrate clear environmental gradients (Vanschoenwinkel et al., 2007; Pandit et al., 2009). Although temporary ponds are widely distributed worldwide (Williams et al., 2001), their high biodiversity contrasts with their sensitivity and vulnerability to external perturbation, which has led to great interest in their conservation over the last few years (Williams et al., 2001; Zacharias et al., 2007; Céréghino et al., 2008). In addition, temporary ponds harbour singular flora and fauna that are often exclusive or infrequently found in permanent ponds (Collinson et al., 1995; Williams, 1997; Céréghino et al., 2008). In particular, their singular macroinvertebrate species can adjust their life cycles to the annual period of pond inundation (hydroperiod), re-starting community assembly after each year's initial inundation (Bazzanti et al., 1996; Boix et al., 2004; Florencio et al., 2009).

In metacommunity ecology, β-diversity, which is the variation in species composition among sites in a geographical area (Legendre et al., 2005; but see e.g. Tuomisto, 2010; Anderson et al., 2011), is a key concept for understanding ecosystem functionality from a management and conservation perspective. In pond networks, environmental heterogeneity has been revealed as crucial in supporting high biodiversity (Urban, 2004; Jeffries, 2005) and also in driving patterns of nested biodiversity, in which species-poor sites contain subsets of species-rich sites, particularly in those systems with good conservation status (Hylander et al., 2005; Florencio et al., 2011). Hence, the study of those species that depart from the expectations of nested biodiversity patterns, which occur more or less frequently than would be predicted in a nested system (termed idiosyncratic), is currently receiving great interest in applied ecology (e.g. Florencio et al., 2011). To better understand the ecological processes maintaining high ecosystem diversity, β-diversity should be partitioned between (i) the β-diversity associated with non-random species loss in nested systems; and (ii) the β-diversity associated with true species replacement (Baselga, 2010). It is essential to disentangle the problem whether β-diversity is driven by species replacement or nestedness to make appropriate conservation decisions. If the former is the driver, it would prioritise the conservation of a large number of sites with variable richness and environmental conditions, while the latter would prioritise the conservation of the richest sites (Baselga, 2010).

One of the main debates in metacommunity ecology involves the relative importance of deterministic, niche-based process (e.g. environmental filters) versus stochastic ecological process (e.g. dispersal filters) in community assembly (Chase & Myers, 2011). Water chemistry and the physical characteristics of ponds each have an important influence on macroinvertebrate composition and abundance in wetlands (Wissinger, 1999; Williams, 2006). Conductivity is one of the most frequent chemical descriptors of macroinvertebrate communities (Garrido & Munilla, 2008; Waterkeyn et al., 2008). In particular, acidic water has negative effects on macroinvertebrate species diversity (Radke et al., 2003). Although nutrient concentrations have controversial effects, they usually negatively impact species occurrences at high levels (Declerck et al., 2005). Applying the theory of island biogeography (MacArthur & Wilson, 1967) to lakes and ponds, high macroinvertebrate and plant species richness is harboured in large ponds (Friday, 1987; Nicolet et al., 2004). Interpond distances can also affect the incidence of species in particular pond assemblages as a result of species dispersal limitations (Briers & Biggs, 2005; Sanderson et al., 2005).

We explored the main drivers of β-diversity and community structure in a macroinvertebrate metacommunity in a pond network of excellent conservation status. This is a highly dynamic system in which thousands of ponds fill and desiccate annually, with only a few ponds retaining water during the summer. The novelty of our study resides in the fact that we obtained comparable data on macroinvertebrates in 80 ponds distributed across an extensive area. We hypothesised that (i) there is high biodiversity in the macroinvertebrate metacommunity, with species replacement and nestedness being the main contributors to β-diversity; (ii) environmental variability is key in maintaining such high macroinvertebrate diversity in the pond network; and (iii) both random (i.e. dispersal) and deterministic processes (i.e. environment) are operating together in the macroinvertebrate assembly. To evaluate these hypotheses, we used data from 80 ponds, collected over a single season, to analyse (i) if β-diversity was mainly sustained by nestedness or by species replacement, and (ii) if spatial connectivity and environmental variability had an important influence on macroinvertebrate structure and β-diversity.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Appendices

Study area

Doñana National Park (SW Spain) is one of the most pristine wetlands in Europe; it was included in the RAMSAR convention in 1982 and was later designated as a World Heritage Site by UNESCO in 1995. This area is located between the mouth of Guadalquivir River and the Atlantic Ocean. In the park, there is a clear geomorphological distinction between the ancient northern area and the southern area, locally known as ‘Marismillas’, which has a more recent marine origin (Siljeström et al., 1994). The three main types of landscapes are as follows: a sandy area with stabilised dunes, a mobile dune system, and an extensive marsh area (see Siljeström et al., 1994 for a detailed geomorphological description of the area). The climate is Mediterranean sub-humid, with mild winters, hot and dry summers, and heavy rains falling mainly in autumn or winter (mean annual rainfall = 544.6 (SD 211.3) mm with significant interannual variability, see Díaz-Paniagua et al., 2010).

This area contains a pond network that is comprised of more than 3000 water bodies in wet years and that is mainly composed of temporary ponds spanning a wide range of hydroperiods (Díaz-Paniagua et al., 2010). These ponds are fed by annual rainfall and a shallow water table that rises above the surface after heavy autumn or winter rainfall and they generally dry out during summer (Díaz-Paniagua et al., 2010). The ponds are heterogeneous in surface area, depth, and hydroperiod and are very abundant in the stabilised dunes and areas of contact between the three types of landscapes (Díaz-Paniagua et al., 2010). In Doñana, there are only two large permanent (or semi-permanent) ponds, which only occasionally dry out after successive years of severe drought. There are also artificially deepened ponds (hereafter referred to as zacallones, the local name) that supply water for cattle and wild fauna during summer. They are present across the whole park but are the main water bodies present in the southern areas. In the contact area between the stable dunes and the freshwater marsh, there are ponds filled by the running water of intermittent streams that mainly flow towards the marsh after heavy rains (hereafter referred to as caños, the local name). This study included ponds that are representative of those in the study area and that were randomly selected across the entirety of the park: 46 temporary ponds, one of the two semi-permanent ponds, 27 zacallones, and 6 caños; we have grouped them according to their location in the northern or southern areas of the park (Fig. 1).

image

Figure 1. Locations of the 80 study ponds in Doñana National Park: 46 temporary ponds, which were mainly located in the northern part of the park, 27 zacallones, which were mainly located in the southern part of the park, 6 caños, and 1 semi-permanent pond are indicated.

Download figure to PowerPoint

Macroinvertebrate sampling and taxon identification

We carried out a single survey of 80 ponds (late March–middle June of 2007) spread across the whole of Doñana National Park (SW Spain) to analyse the environmental and spatial effects operating over the minimum time window in which all sites could be visited. We determined the presence or absence of macroinvertebrates using a dip net (39 × 21 cm, 1 mm mesh size) and netting across a stretch of water of approximately 1.5 m length in each sampling unit. In each pond, we sampled all different available microhabitats, based largely on differences in aquatic plant cover and depth (Heyer et al., 1994). As the efficiency of dip netting increases in small ponds (Heyer et al., 1994), we took more samples in larger ponds, which also typically contained a higher number of microhabitats, to achieve a comparable effort in detecting rare species (samples per pond ranged from 3 to 13). Sampling appropriateness was supported by a previous study in which similar results were obtained for sample-based rarefaction and raw data (see Florencio et al., 2011 for details). Most macroinvertebrates were identified in situ and then released again into the pond. Only unidentified individuals were preserved in 70% ethanol for later identification in the laboratory. We identified individuals to the species or genus level, except for Basomatophora, Diptera, Oligochaeta, and saldid bugs, which were identified to the family level (see Appendix 1 for the detailed taxonomic list). For those species for which we identified larvae and adults, we considered both stages separately in our analyses because of their different environmental requirements; they are thus referred to as taxa stages in our data.

Environmental variables in the extensive macroinvertebrate survey

To characterise the environmental gradients in Doñana ponds, we considered different groups of variables.

Environmental variables

In the field, we visually identified all the different plant taxa (species or genus level) per pond to estimate plant richness (Rplant). Maximum water depth (Max depth) was measured at the deepest point of the pond with a graduated pole. Pond surface area, the total number of ponds with an extension >150 m2 into a 200 m buffer area around each pond, and the total flooded surface area in a 200 m buffer area around each pond were extracted from a GIS-based map of ponds obtained from a hyperspectral image taken at a moment of high inundation of the area (see Gómez-Rodríguez et al., 2008 for details). We recorded in situ pH (near the bed using pH meter HI 991000, HANNA instruments, Portugal), dissolved oxygen concentration (near the bed using YSI 550A Handheld Dissolved Oxygen and Temperature System, YSI Incorporated, Yellow Springs, OH, USA), electrical conductivity (EC) at 20 °C (near the bed using Multi-range Conductivity Meter HI 9033, HANNA instruments, Romania), and turbidity (in the water column using Loggin Microprocessor turbidity meter HI 93703-11, HANNA instruments, Hungary). Surface water (500 ml) was collected to determine nutrient concentrations (dissolved inorganic phosphate, nitrate, nitrite, and ammonium), alkalinity, and main cation and anion concentrations (Cl, Na+, Ca2+, K+ and Mg2+). Ion concentrations were analysed using a Trace Inductively Coupled Plasma Mass Spectrometer, while nutrient concentrations were measured colourimetrically using an Auto Analyser (Bran+Luebbe). Alkalinity was analysed according to the titration method described in APHA (1998). Surface sediment samples (5 cm depth) were collected and the following variables were measured in the laboratory: organic matter (three replicates; lost on ignition, 450 °C, 5 h) and sediment total P (two replicates). Sediment total P was estimated using dissolved inorganic phosphate obtained following the method of Murphy and Riley (1962), in which the ignited sediment undergoes acid digestion with 0.5 M H2SO4 and K2S2O8 (0.5–1 g) at 120 °C for 4 h (Golterman, 2004). The total Fe concentration in digested sediment (two replicates) was determined colourimetrically by means of o-phenantroline and using ascorbic acid as the reducing agent (Golterman, 2004). The Na+/Ca2+ ratio was measured because of its biological implications in regulating processes associated with the acid-base balance of the organisms (Radke et al., 2003). We did not use nitrite and nitrate concentrations in the analyses because most values were negligible (range <0.15–0.60 mg l−1).

Marsh-coast distance variables

To account for the influence of potential external sources of organisms (see e.g. Fahd et al., 2007), we measured the minimum linear distances from each pond to the border of the marsh (Dmarsh) and the coast (Dcoast); these distances were also estimated using the GIS pond map (see Gómez-Rodríguez et al., 2008 for details).

Spatial variables

Seventy-nine orthogonal spatial descriptors based on interpond distances were generated using Moran's eigenvector maps (MEMs) in R software 2.11.1 (R Development Core Team, 2010) (‘spacemakeR’ package, Dray, 2010; see Dray et al., 2006), which provide a general framework of principal coordinates of neighbour matrices (see Borcard & Legendre, 2002). The spatial descriptors extracted from the MEMs were ordered from V1 to V79, i.e. from the highest to the lowest eigenvalues. A selection of spatial descriptors that controlled for Type I error in the analyses was carried out according to Peres-Neto and Legendre (2010). The number of spatial descriptors was reduced using the ‘ortho.AIC’ command in R software 2.11.1 (R Development Core Team, 2010) (‘spacemakeR’ package, Dray, 2010). Only significant spatial descriptors with positive eigenvalues were considered in the analyses described below [redundancy analysis (RDA) and variation partitioning] to evaluate the effect of interpond distances on the structure of macroinvertebrate assemblages.

Statistical analyses

We constructed a pond-characteristic matrix with the values of the environmental and marsh-coast distance variables. In addition, each group of variables (environmental, marsh-coast distance, and spatial) was considered in three individual matrices. Each variable had been previously transformed to approximate normality (Appendix 2). To obtain the pond-characteristic resemblance matrix, Euclidean distance was applied to the pond-characteristic matrix (Legendre & Legendre, 1998). Finally, we constructed a macroinvertebrate matrix that included the number of samples in which every taxa stage was present divided by the total number of samples taken in a pond. The Bray-Curtis index was applied to the macroinvertebrate matrix to obtain the macroinvertebrate resemblance matrix (Legendre & Legendre, 1998). Subsets of the macroinvertebrate matrix were extracted for the main taxonomical orders Coleoptera, Heteroptera, and Odonata.

To visualise the environmental variability in pond characteristics, we represented the pond dissimilarities by performing non-metric multidimensional scaling (NMDS) in PRIMER version 6 (Clarke & Warwick, 2001) on the pond-characteristic matrix.

We calculated the mean pair-wise macroinvertebrate β-diversity (βsor) in our extensive sampling survey data to analyse macroinvertebrate β-diversity in the study area. The Sørensen index was applied to the presence-absence data (Legendre & Legendre, 1998). Using R software 2.11.1 (R Development Core Team, 2010), we partitioned βsor into β-diversity associated with species replacement (βsim) and β-diversity associated with nestedness (βnes) using the pair-wise measure approach described in Baselga (2010). In short, the total dissimilarity between each pair of ponds (βsor) was partitioned into two additive components accounting for dissimilarity due to species replacement (βsim) and dissimilarity due to nestedness (βnes), respectively, following the formula βsor βsim + βnes (Baselga, 2010). We also performed β-diversity partitioning using monthly macroinvertebrate assemblages of 22 of the temporary ponds for 2 years with different rainfalls (see Florencio et al., 2009, 2011 for a detailed description of macroinvertebrate sampling). As we obtained similar results, these data are not presented here for the sake of simplicity.

To detect which environmental variables influenced the macroinvertebrate assemblage structure of ponds, we performed constrained ordination using RDA in R software 2.11.1 (R Development Core Team, 2010) (‘vegan’ package, Oksanen et al., 2010) on each of the environmental, marsh-coast distance, and spatial variable matrices and the macroinvertebrate matrix and, independently, on the Coleoptera, Odonata and Heteroptera matrices. In the RDA, we excluded taxa stages that occurred in less than five ponds (30% of total species number) to avoid the disrupting effect of rare species (Leps & Smilauer, 2003). To exclude co-variables found to have poor explanatory power in RDA, we performed Spearman rank correlations (rs) between each pair of environmental variables (Appendix 2). We used a forward stepwise procedure to select environmental variables, as described in Blanchet et al. (2008). Variation partitioning was performed in R software 2.11.1 (R Development Core Team, 2010) (‘vegan’ package, Oksanen et al., 2010) to measure the independent effects of environmental, marsh-coast distance, and spatial variables (see Borcard et al., 1992); only explanatory variables found to be significant were extracted from RDA and included. In our variation partitioning, we used the adjusted multiple coefficient of determination (Adj. r2), as required when matrices have different numbers of variables (Peres-Neto et al., 2006). Significances were tested using Monte Carlo permutation tests (999 permutations).

After the RDA were performed, the influence of the significant explanatory variables on particular taxa stages and assemblages was analysed by performing a linktree analysis in PRIMER version 6 (Clarke & Warwick, 2001) on the macroinvertebrate matrix (our parameterisation used three as the minimum group size and four as the minimum split size). simprof analyses retained divisions significant at the 0.05 level and yielded a dendrogram of the results, otherwise known as a linkage tree (Clarke et al., 2008). The pair-wise differences between each group of macroinvertebrate assemblages detected by the linkage tree were assessed using one-way anosim analysis (anosim statistic R is close to one when groups are completely different). We determined which taxa stages contributed the most to these pair-wise differences (>10% of contribution) using one-way simper analysis in PRIMER version 6 (Clarke & Warwick, 2001).

To explore the relative influence of environmental variables on the partitioning of β-diversity, we used multiple regression on distance matrices (MRM), an extension of Mantel test (Legendre et al., 1994). Spearman correlations (rs) were used in the MRM analyses. Significant explanatory variables were identified using a forward-selection procedure (Legendre et al., 1994). The significance of MRM models was assessed using 1000 permutations and only the most significant non-correlated variables were retained within each group of variables (spatial descriptors, environmental and marsh-coast distance variables). We constructed three successive models: (i) the spatial model, which used significant spatial descriptors to measure spatial influences on β-diversity; (ii) the spatial/environmental model, which used significant environmental variables in addition to spatial descriptors to obtain partial effects; (iii) the spatial/environmental/marsh-coast distance model, which added significant marsh-coast distance variables to the previous model. All these calculations were performed using R software 2.11.1 (R Development Core Team, 2010; ‘ecodist’ package, Goslee & Urban, 2007).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Appendices

Variability in pond characteristics

The NMDS representation of the environmental variables of the sampled ponds revealed a heterogeneous pond network (Fig. 2). Northern ponds, which were mainly temporary, evidenced their high environmental variability when compared with southern ponds, which were mainly zacallones (Fig. 2).

image

Figure 2. Non-metric multidimensional scaling ordination of the 80 study ponds according to the pond-characteristic resemblance matrix (Euclidean distance). Temporary ponds (Temp), zacallones (z), caños (Caño), and the semi-permanent pond (Semip) are highlighted as well as the location of ponds in southern (South) and northern (North) areas of the park.

Download figure to PowerPoint

Macroinvertebrate β-diversity partitioning

We recorded 135 taxa stages across the 80 study ponds, with an average of 23.5 (SD 8.5) taxa stages per pond. Using our extensive macroinvertebrate survey, we found that β-diversity was important in the study area [pond average of βsor = 0.65 (SD 0.11)]. In β-diversity partitioning, species replacement contributed more to β-diversity [pond average of βsim = 0.52 (SD 0.14)] than nestedness [pond average of βnes = 0.13 (SD 0.10)].

Pond environmental variables influencing macroinvertebrate community structure

The significant explanatory variables influencing pond macroinvertebrate compositions detected by RDA were EC, Rplant, and Max depth among the environmental variables; both Dmarsh and Dcoast; and two spatial descriptors with high eigenvalues, V2 and V5. These high eigenvalues implied large interpond distances were involved (Table 1). EC had the greatest effect on the macroinvertebrate community (Table 1).

Table 1. Significant explanatory variables emerging from redundancy analyses (on the whole macroinvertebrate community and Coleoptera, Odonata, and Heteroptera matrices) performed independently on environmental, marsh-coast distance, and spatial variablesa
Explanatory variablesCommunityColeopteraOdonataHeteroptera
  1. n.s., non-significant variables; _, excluded variables; EC, electrical conductivity; Rplant, plant richness; Max depth, maximum water depth; Pond area, pond surface area; Pond number, total number of ponds with an extension > 150 m2 into a 200 m buffer area around each pond; Dmarsh, minimum linear distances from each pond to the border of the marsh; Dcoast, minimum linear distances from each pond to the coast; V5, V2, eigenvectors extracted from the inter-pond distance based on the Moran's eigenvector maps.

  2. a

    Values are the explained variance, indicating the magnitude of the effects of each significant explanatory variable, and global F-ratios.

  3. b

    P < 0.05.

  4. c

    P < 0.01.

EnvironmentalF-ratio = 3.347cF-ratio = 3.23cF-ratio = 4.45bF-ratio = 4.85c
EC0.34b0.02b_0.25c
Rplant0.18b__0.15c
Max depth0.17b0.02cn.s.0.10b
Pond area_0.03cn.s._
Na+/Ca2+ ration.s.n.s.n.s.0.08b
Pond number___0.08b
Alkalinity__0.05b_
Marsh-coast distanceF-ratio = 2.898cF-ratio = 2.75cF-ratio = 3.64bn.s.
Dmarsh0.26c0.02c0.04bn.s.
Dcoast0.17b0.03c_n.s.
SpatialF-ratio = 2.423cF-ratio = 1.89bn.s.n.s.
V50.17b0.02bn.s.n.s.
V20.15bn.s.n.s.n.s.

Coleopterans averaged 10.8 (SD 5.2) taxa stages per pond. We found that three groups of environmental variables had important effects on the structure of coleopteran assemblages. EC, Max depth, and Pond surface area were the significant environmental explanatory variables; both marsh-coast distance variables, Dmarsh and Dcoast, were significant; and only the spatial descriptor V5 had a significant effect among the spatial variables (Table 1). Odonatan assemblages [average = 2.4 (SD 2.1) taxa per pond] were significantly explained by Alkalinity and Dmarsh; no spatial descriptors were significant explanatory variables (all P > 0.36, Table 1). For heteropteran assemblages [average = 7.1 (SD 2.8) taxa stages per pond], EC, Rplant, Max depth, Na+/Ca2+ ratio, and the total number of ponds with an extension >150 m2 into a 200 m buffer area around each pond were significant environmental explanatory variables; no marsh-coast distance variables or spatial descriptors were significant explanatory variables (all P > 0.09, Table 1).

Variation partitioning analyses revealed that environmental variables (EC, Rplant and Max depth) had a more important effect on macroinvertebrate assemblage structures than marsh-coast distance and spatial variables (Table 2). Environmental variables were also the most important explanatory variables in coleopteran (EC, Max depth and Pond surface area), odonatan (Alkalinity), and heteropteran assemblages (EC, Rplant, Max depth, Na+/Ca2+ ratio in water column, and the total number of ponds with an extension >150 m2 into a 200 m buffer area around each pond) (Table 2). There were no significant independent effects of marsh-coast distance variables and spatial descriptors on the structure of odonatan and heteropteran assemblages (Table 2).

Table 2. Independent effects of environmental, marsh-coast distance, and spatial variables on macroinvertebrate community structure and Coleoptera, Odonata, and Heteroptera matrices as indicated by variation partitioning analyses
Adj. r2aCommunityColeopteraOdonataHeteroptera
  1. a

    Adjusted r2 (ranged 0–1).

  2. b

    < 0.05.

  3. c

    P < 0.01.

Environmen-tal0.10c0.07c0.04c0.20c
Marsh-coast distance0.05c0.03cn.s._
Spatial0.02c0.01b__

Pond macroinvertebrate assemblages and environmental thresholds

The linkage tree differentiated 16 pond groups based on differences in EC, Max depth, Dcoast, Dmarsh, Rplant, and pond macroinvertebrate assemblages. Macroinvertebrate assemblages associated with these pond groups differed in their contribution to the global dissimilarity of the whole macroinvertebrate community (Fig. 3). Fourteen taxa stages were the main contributors to pond assemblage differences along a generalist-specialist gradient; species ranged from occurring in several different types of environments to only being recorded under specialised conditions (Fig. 3). Four generalist taxa stages occurred under multiple environments (adults of Corixa affinis Leach, 1817; adults of Anisops sardeus Herrich-Schäffer, 1849; larvae of Cloeon Leach, 1815 spp.; and larvae of Notonectidae); another five taxa stages were favoured by narrower environmental conditions [Sympetrum fonscolombei (Selys, 1841) larvae; Chironomus plumosus (Linneo, 1758) larvae; Anacaena lutescens (Stephens, 1829) adults; Gerris thoracicus Schummel, 1832 adults; and Gerris spp. larvae]. Five further specialist taxa stages were the main species contributing to the differentiation of ponds with particular characteristics: adults of Hydrobius fuscipes (Linnaeus, 1758) and Limnoxenus niger (Zschach, 1788) as well as Corixidae larvae and Culicidae larvae mainly occurred in shallow waters; Plea minutissima Leach, 1817 adults mainly occurred in deep ponds far from the coast with poor Rplant; and Sigara lateralis (Leach, 1817) adults were also common in deep waters with poor Rplant but that were close to the coast and far from the marsh (Fig. 3). Sympetrum fonscolombei larvae, Gerris spp. larvae, G. thoracicus adults, A. lutescens adults, H. fuscipes/L. niger adults, Corixidae larvae, C. plumosus larvae, Culicidae larvae, and P. minutissima adults made the greatest contributions to the global dissimilarity of the community, whilst S. lateralis adults contributed the least (Fig. 3).

image

Figure 3. Linkage tree representation showing significant divisive clustering of pond macroinvertebrate assemblages constrained by the significant environmental and marsh-coast distance variables detected by redundancy analyses: electrical conductivity (μS cm−1), maximum depth (cm), distance to the coast (Kmc), distance to the marsh (Kmm), and plant richness (Rplant). Pond number is indicated in each split group. Each successive split is conditioned by the indicated environmental thresholds of previous splits. B% is the contribution of each binary partition to global dissimilarity (ranged 0–100%). Capital letters indicate main taxa-stage contributors (>10% of contribution) to each split group (-A is adults, -L is larvae). R is the Spearman coefficient giving the dissimilarity value in every split.

Download figure to PowerPoint

Environmental variables driving the macroinvertebrate β-diversity pattern

Among the variables included in the MRM analyses, only NH4 influenced the βnes pattern observed in the pond network (rs = 0.13, r2 = 0.017, P < 0.05). With respect to the explanatory variables of βsim involved in β-diversity, two spatial descriptors with high eigenvalues (V1 and V5) were found to be significant variables in the spatial model (Table 3). In the spatial/environmental model, V5 and Alkalinity had the highest coefficients and thus best explained the βsim values (Table 3). In the spatial/environmental/marsh-coast distance model, V5 and Alkalinity were retained and shared similar, high coefficients that revealed their influence on βsim (Table 3). The spatial/environmental/marsh-coast distance model, which included the highest number of significant variables, explained 9% of variation in βsim (r2 = 0.09, Table 3).

Table 3. Different multiple regression models associating macroinvertebrate β-diversity to the species replacement and including only spatial variables; spatial and environmental variables; and spatial, environmental, and marsh-coast distance variables
Variablersa
  1. m.s., marginally significant (P = 0.057); Rplant, plant richness; Max depth, maximum water depth; Dcoast, minimum linear distances from each pond to the coast; Dmarsh, minimum linear distances from each pond to the border of the marsh.

  2. a

    Coefficients of Spearman correlations, r2 (ranged 0–1), 1000 permutations.

  3. b

    Eigenvectors extracted from the interpond distance based on the Moran's eigenvector maps.

  4. c

    P < 0.05.

  5. d

    P < 0.01.

Spatial modelr2 = 0.043d
V1b0.153d
V5b0.140d
Spatial/environmental modelr2 = 0.074d
V10.077 m.s.
V50.139d
Alkalinity0.141d
Rplant0.0949d
Max depth0.069c
Spatial/environmental/marsh-coast distance modelr2 = 0.090d
V10.080c
V50.130d
Alk0.136d
Rplant0.086c
Max depth0.063c
Dcoast0.094c
Dmarsh0.084c

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Appendices

Environmental variability

The high dissimilarity detected in the environmental characteristics of Doñana ponds reveals this system to be highly heterogeneous; this pattern is particularly due to the wide variability observed among temporary ponds, which are the most abundant aquatic habitats in this area. Although the artificially deepened ponds (zacallones) in the southern area of the park were more similar in their environmental characteristics, they widely differed from northern water bodies, thus increasing the heterogeneity of the total pond network. The long hydroperiod of these zacallones extends the temporal availability of aquatic habitats in the area and thus they act as reservoirs for species typical of temporary ponds, mainly macroinvertebrate dispersers that are forced to leave drying ponds in summer (see e.g. Garrido & Munilla, 2008; Florencio et al., 2009).

Macroinvertebrate β-diversity in a heterogeneous pond network

The macroinvertebrate β-diversity pattern reveals a diverse system mainly driven by species replacement in the pond network. Although the macroinvertebrate community of the Doñana pond network has been described as having a clear nested pattern (Florencio et al., 2011), β-diversity partitioning indicated that nestedness hardly contributed to macroinvertebrate β-diversity. The relative importance of species replacement to β-diversity described in this study is concordant with the high number of idiosyncratic taxa stages (59) and ponds (34) found in the area that departed from the general nested pattern (Florencio et al., 2011).

We detected some species whose occurrence was associated with particular environmental conditions, supporting the role of pond environmental heterogeneity in driving species replacement. In the linkage tree, we detected 10 taxa that were specialists occurring in a narrow range of environmental variability. These 10 specialists, with the exception of Gerris spp. larvae, were all included in the 59 idiosyncratic taxa stages listed for the Doñana pond network (see Florencio et al., 2011). Except for S. lateralis, these taxa stages significantly contributed to the global dissimilarity of the whole macroinvertebrate community.

Relationships between macroinvertebrate assemblages and pond characteristics

We found similar explanatory variables influencing the macroinvertebrate community structure and the β-diversity associated with species replacement: EC (and alkalinity as a co-variable), maximum depth, aquatic plant richness, and distance from the ponds to the marsh and the coast, and two spatial descriptors. In metacommunity ecology, patterns of β-diversity are usually driven by biogeographical conditions (i.e. closer ponds should be more similar than more distant ponds as a result of species dispersal capabilities) as well as by environmental heterogeneity associated with complex processes (Leibold et al., 2004; Legendre et al., 2005). In this study, we found that one spatial descriptor as well as pond variability in alkalinity (and the co-variables EC, pH, and ion concentration) drove the macroinvertebrate β-diversity pattern via species replacement. Therefore, spatial and environmental filters are operating in community assembly via dispersal and species-sorting respectively (Patrick & Swam, 2011). These results are concordant with Chase and Myers (2011)'s predictions: β-diversity increases across spatial gradients in accordance with stochastic dispersal processes and β-diversity increases across environmental gradients in accordance with the niche-based theory.

In this study, EC (and co-variables) was correlated with the distance of ponds from the coast, revealing a gradual increase in water conductivity values from the north to the south of the park (82–8800 μS cm−1). The study ponds have no surface or groundwater connection to the sea though they have some oceanic influence due to airbone sea salt deposition and so the closer to the coast the higher the electrical conductivity. This conductivity gradient thus influences both the macroinvertebrate community structure and the β-diversity pattern. We also found that some species typically occurred under low conductivity conditions, for example A. lutescens occurred in waters with values lower than 225 μS cm−1. Similarly, in other temporary water systems, different species can occur across wide conductivity gradients (see e.g. Gutiérrez-Estrada & Bilton, 2010). The occurrence of different species can be favoured at different values of the conductivity gradient. For example we observed that Heteropterans, for example the corixid S. lateralis, preferred southern zacallones, which exhibited the highest conductivity in the study area. In contrast, Odonatans preferred northern temporary ponds with the lowest conductivity values; for example S. fonscolombei was observed almost exclusively in these ponds.

When exploring the influence of interpond distances on macroinvertebrate assemblage structure and macroinvertebrate β-diversity, we only obtained spatial descriptors with high eigenvalues, a result that, in natural systems, signifies the involvement of broad spatial scales (Borcard & Legendre, 2002; Diniz-Filho & Bini, 2005; Griffith & Peres-Neto, 2006). Therefore, in this study, only the largest interpond distances had an effect on the macroinvertebrate assemblages and thus also on macroinvertebrate β-diversity, resulting in a system with high connectivity where species demonstrated weak dispersal limitations. The excellent dispersal abilities that usually characterise species of temporary ponds and let them cope with pond desiccation (Williams, 2006) largely contributed to the weak dispersal limitations in the study area. The Doñana pond network has already been determined to be a robust network for amphibian species, allowing them to encounter reproduction habitats even in extremely dry years (Fortuna et al., 2006); we confirm in this study that this assessment also applies to macroinvertebrate species.

We identified aquatic vegetation as an important variable structuring the macroinvertebrate community and its diversity, which is concordant with other studies carried out in temporary waters (e.g. Nicolet et al., 2004; Bilton et al., 2009). Diverse vegetation offers a wide range of niches for macroinvertebrate species, with a high number of refuges for species under predation and food availability for grazers. Plant species' architecture can influence biological processes, for example predator–prey interactions and the presence of oviposition sites. Hence, in this study, ponds with high aquatic plant richness harboured distinct macroinvertebrate assemblages that contributed significantly to macroinvertebrate diversity as a whole. In addition to aquatic vegetation, other biotic factors can affect the macroinvertebrate communities of temporary ponds; for instance, predators may have a seasonal effect, which could constitute an important focus for further research.

Implications for conservation

We demonstrate that both stochastic and deterministic ecological processes can operate together to assemble macroinvertebrates in a pond network. Stochastic processes such as dispersal only influenced the macroinvertebrate community and β-diversity at large spatial scales, which reveal the high connectivity of the system. Environmental variability was consequently key in maintaining high biodiversity in this system. The macroinvertebrate β-diversity pattern was mainly driven by species replacement, with different species occurring in different environments; in contrast, the contribution of nestedness to β-diversity was low. Although the Doñana pond network has been described as having a clear nested pattern, the species that most contributed to β-diversity were largely idiosyncratic species and thus departed from the general nested pattern. We found that these idiosyncratic species occurred in specialised environments and were predominantly responsible for maintaining the system's high biodiversity. In this study, we demonstrate the importance of idiosyncratic species in sustaining diversity in nested systems that contain high numbers of idiosyncratic species. Therefore, the best strategy for conservation is to preserve diverse environments across a non-fragmented habitat where species are not limited by dispersal. In other words, it is preferable to protect a wide range of diverse and interconnected ponds rather than the richest ones, which would be the conservation priority in a strictly nested system.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Appendices

We thank Azahara Gómez, Alexandre Portheault, and Carlos Marfil for assistance with fieldwork, and Cayetano Gutiérrez Canovas for assistance in β-diversity analyses. David Bilton supervised prior analyses and gave us interesting suggestions. Stephanie Gascón provided statistical tools. Andrés Millán collaborated in taxonomical identification. Jessica Pearce helped edit the English. The Spanish Ministry of Science and Innovation and European Union Social Fund (CGL2006-04458/BOS and Fellowship grants CSIC-I3P to M.F.), Junta de Andalucía (Excellence Research Project 932 and PAI Group RNM128) and Ministerio de Medio Ambiente, Medio Rural y Marino (Research Project 158/2010) provided funds for this study.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Appendices
  • Anderson, M.J., Crist, T.O., Chase, J.M., Vellend, M., Inouye, B.D., Freestone, A.L., Sanders, N.J., Cornell, H.V., Comita, L.S., Davies, K.F., Harrison, S.P., Kraft, N.J.B., Stegen, J.C. & Swenson, N.G. (2011) Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecology Letters, 14, 1928.
  • APHA (1998) Standard Methods for the Examination of Water and Wastewater, 20th edn. American Public Health Association, Washington, District of Columbia.
  • Baselga, A. (2010) Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography, 19, 134143.
  • Bazzanti, M., Baldoni, S. & Seminara, M. (1996) Invertebrate macrofauna of a temporary pond in Central Italy: composition, community parameters and temporal succession. Archiv für Hydrobiologie, 137, 7794.
  • Bilton, D.T., McAbendroth, L., Nicolet, P., Bedford, A., Rundle, S.D., Foggo, A. & Ramsay, P.M. (2009) Ecology and conservation status of temporary and fluctuating ponds in two areas of Southern England. Aquatic Conservation: Marine and Freshwater Ecosystems, 19, 134146.
  • Blanchet, F.G., Legendre, P. & Borcard, D. (2008) Forward selection of explanatory variables. Ecology, 89, 26232632.
  • Boix, D., Sala, J., Quintana, X.D. & Moreno-Amichi, R. (2004) Succession of the animal community in a Mediterranean temporary pond. Journal of the North American Benthological Society, 23, 2949.
  • Borcard, D. & Legendre, P. (2002) All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153, 5168.
  • Borcard, D., Legendre, P. & Drapeau, P. (1992) Partialling out the spatial component of ecological variation. Ecology, 73, 10451055.
  • Briers, R.S. & Biggs, J. (2005) Spatial patterns in pond invertebrate communities: separating environmental and distance effects. Aquatic Conservation: Marine and Freshwater Ecosystems, 15, 549557.
  • Céréghino, R., Biggs, J., Oertli, B. & Declerck, S. (2008) The ecology of European ponds: defining the characteristics of a neglected freshwater habitat. Hydrobiologia, 597, 16.
  • Chase, J.M. & Myers, J.A. (2011) Disentangling the importance of ecological niches from stochastic processes across scales. Philosophical Transactions of the Royal Society B, 366, 23512363.
  • Clarke, K.R., Somerfield, P.J. & Gorley, R.N. (2008) Testing of null hypotheses in explanatory community analyses: similarity profiles and biota-environment linkage. Journal of Experimental Marine Biology and Ecology, 366, 5669.
  • Clarke, K.R. & Warwick, R.M. (2001) Change in Marine Communities: An Approach to Statistical Analysis and Interpretation, 2nd edn. PRIMER-E, Plymouth, UK.
  • Collinson, N.H., Biggs, J., Corfield, A., Hodson, M.J., Walker, D., Whitfield, M. & Williams, P.J. (1995) Temporary and permanent ponds: an assessment of the effects of drying out on the conservation value of aquatic macroinvertebrate communities. Biological Conservation, 74, 125133.
  • Declerck, S., Vandekerkhove, J., Johansson, L., Muylaert, K., Conde-Porcuna, J.M., Van Der Gucht, K., Pérez-Martínez, C., Lauridsen, T., Schwenk, K., Zwart, G., Rommens, W., López-Ramos, J., Jeppesen, E., Vyverman, W., Brendonck, L. & De Meester, L. (2005) Multi-group biodiversity in shallow lakes along gradients of phosphorus and water plant cover. Ecology, 86, 19051915.
  • Díaz-Paniagua, C., Fernández-Zamudio, R., Florencio, M., García-Murillo, P., Gómez-Rodriguez, C., Siljestrom, P. & Serrano, L. (2010) Temporary ponds from the Doñana National Park: a system of natural habitats for the preservation of aquatic flora and fauna. Limnetica, 29, 118.
  • Diniz-Filho, J.A.F. & Bini, L.M. (2005) Modelling geographical patterns in species richness using eigenvector-based spatial filters. Global Ecology and Biogeography, 14, 177185.
  • Dray, S. (2010) spacemakeR: spatial modelling. R package version 0.0-5/r83. <http://R-Forge.R-project.org/projects/sedar/> 7th March 2011.
  • Dray, S., Legendre, P. & Peres-Neto, P.R. (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modelling, 196, 483493.
  • Fahd, K., Florencio, M., Keller, C. & Serrano, L. (2007) The effect of the sampling scale on zooplankton community assessment and its implications for the conservation of temporary ponds in south-west Spain. Aquatic Conservation: Marine and Freshwater Ecosystems, 17, 175193.
  • Florencio, M., Díaz-Paniagua, C., Serrano, L. & Bilton, D.T. (2011) Spatio-temporal nested patterns in macroinvertebrate assemblages across a pond network with a wide hydroperiod range. Oecologia, 166, 469483.
  • Florencio, M., Serrano, L., Gómez-Rodríguez, C., Millán, A. & Díaz-Paniagua, C. (2009) Inter and intra-annual variations of macroinvertebrate assemblages are related to the hydroperiod in Mediterranean temporary ponds. Hydrobiologia, 634, 167183.
  • Fortuna, M.A., Gómez-Rodríguez, C. & Bascompte, J. (2006) Spatial network structure and amphibian persistence in stochastic environments. Proceedings of the Royal Society B: Biological Sciences, 273, 14291434.
  • Friday, L.E. (1987) The diversity of macroinvertebrate and macrophyte communities in ponds. Freshwater Biology, 18, 87104.
  • Garrido, J. & Munilla, I. (2008) Aquatic Coleoptera and Hemiptera assemblages in three coastal lagoons of the NW Iberian Peninsula: assessment of conservation value and response to environmental factors. Aquatic Conservation: Marine and Freshwater Ecosystems, 18, 557569.
  • Golterman, H.L. (2004) The Chemistry of Phosphate and Nitrogen Compounds in Sediments. Kluwer Academic Publisher, Dordrecht.
  • Gómez-Rodríguez, C., Bustamante, J., Koponen, S. & Díaz-Paniagua, C. (2008) High-resolution remote-sensing data in amphibian studies: identification of breeding sites and contribution to habitat models. Herpetological Journal, 18, 103113.
  • Goslee, S.C. & Urban, D.L. (2007) The ecodist package for dissimilarity-based analysis of ecological data. Journal of Statistical Software, 22, 119. <http://cran.r-project.org/> 14th October 2011.
  • Griffith, D.A. & Peres-Neto, P.R. (2006) Spatial modelling in ecology: the flexibility of eigenfunction spatial analyses. Ecology, 87, 26032613.
  • Gutiérrez-Estrada, J.C. & Bilton, D.T. (2010) A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters. Ecological Modelling, 221, 14511462.
  • Heyer, W.R., Donnelly, M.A., McDiarmid, R.W., Hayek, L.C. & Foster, M.S. (1994) Measuring and Monitoring Biological Diversity. Standard Methods for Amphibians. Smithsonian Institution Press, Washington, District of Columbia.
  • Hylander, K., Nilsson, C., Jonsson, B.G. & Göthner, T. (2005) Differences in habitat quality explain nestedness in a land snail meta-community. Oikos, 108, 351361.
  • Jeffries, M. (2005) Small ponds and big landscapes: the challenge of invertebrate spatial and temporal dynamics for European pond conservation. Aquatic Conservation: Marine and Freshwater Ecosystems, 15, 541547.
  • Legendre, P., Lapointe, F.-J. & Casgrain, P. (1994) Modelling brain evolution from behavior: a permutational regression approach. Evolution, 48, 14871499.
  • Legendre, P., Lapointe, F.-J. & Casgrain, P. (2005) Analyzing beta diversity: Partitioning the spatial variation of community composition data. Ecological Monographs, 75, 435450.
  • Legendre, P. & Legendre, L. (1998) Chapter 7: ecological resemblance. Numerical Ecology: Developments in Environmental Modelling 20 (ed. by P. Legendre and L. Legendre), 2nd edn., pp. 247302. Elsevier, Amsterdam, the Netherlands & New York City, New York.
  • Leibold, M.A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase, J.M., Hoopes, M.F., Holt, R.D., Shurin, J.B., Law, R., Tilman, D., Loreau, M. & Gonzalez, A. (2004) The metacommunity concept: a framework for multi-scale community ecology. Ecology Letter, 7, 601613.
  • Leps, J. & Smilauer, P. (2003) Multivariate Analysis of Ecological Data Using CANOCO. Cambridge University Press, New York City, New York.
  • MacArthur, R.H. & Wilson, E.O. (1967) The Theory of Island Biogeography. Princenton University Press, Princenton, New Jersey.
  • Murphy, J. & Riley, J.P. (1962) A modified single solution method for the determination of soluble phosphate in natural waters. Analytica Chimica Acta, 37, 3136.
  • Nicolet, P., Biggs, J., Fox, G., Hodson, M.J., Reynolds, C., Whitfield, M. & Williams, P. (2004) The wetland plant and macroinvertebrate assemblages of temporary ponds in England and Wales. Biological Conservation, 120, 261278.
  • Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., O'Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H. & Wagner, H. (2010) vegan: community ecology package. R package version 1.18-13/r1328.<http://R-Forge.R-project.org/projects/vegan/> 7th March 2011.
  • Pandit, S.N., Kolasa, J. & Cottenie, K. (2009) Contrasts between habitat generalists and specialists: an empirical extension to the basic metacommunity framework. Ecology, 90, 22532262.
  • Patrick, C.J. & Swam, C.M. (2011) Reconstructing the assembly of a stream-insect metacommunity. Journal of the North American Benthological Society, 30, 259272.
  • Peres-Neto, P.R. & Legendre, P. (2010) Estimating and controlling for spatial structure in the study of ecological communities. Global Ecology and Biogreography, 19, 174184.
  • Peres-Neto, P.R., Legendre, P., Dray, S. & Borcard, D. (2006) Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology, 87, 26142625.
  • R Development Core Team (2010). R: a language and environment for statistical computing. R Foundation for Statistical Computing. <http://www.R-project.org> 14 October 2011.
  • Radke, L.C., Juggins, S., Halse, S.A., De Deckker, P. & Finston, T. (2003) Chemical diversity in south-eastern Australia saline lakes II: biotic implications. Marine and Freshwater Research, 54, 895912.
  • Sanderson, R.A., Eyre, M.D. & Rushton, S.P. (2005) Distribution of selected macroinvertebrates in a mosaic of temporary and permanent freshwater ponds as explained by autologist models. Ecography, 28, 355362.
  • Siljeström, P.A., Moreno, A., García, L.V. & Clemente, L.E. (1994) Doñana National Park (south-west Spain): geomorphological characterization through a soil-vegetation study. Journal of Arid Environments, 26, 315323.
  • Tuomisto, H. (2010) A diversity of beta diversities: straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography, 33, 222.
  • Urban, M.C. (2004) Disturbance heterogeneity determines freshwater metacommunity structure. Ecology, 85, 29712978.
  • Urban, M.C. & Skelly, D.K. (2006) Evolving metacommunities: toward an evolutionary perspective on metacommunities (Concepts & Synthesis). Ecology, 87, 16161626.
  • Vanschoenwinkel, B., De Vries, C., Seaman, M. & Brendonck, L. (2007) The role of metacommunity processes in shaping invertebrate rock pool communities along a dispersal gradient. Oikos, 116, 12551266.
  • Waterkeyn, A., Grillas, P., Vanschoenwinkel, B. & Brendonck, L. (2008) Invertebrate community patterns in Mediterranean temporary wetlands along hydroperiod and salinity gradients. Freshwater Biology, 53, 18081822.
  • Williams, D.D. (1997) Temporary ponds and their invertebrate communities. Aquatic Conservation: Marine and Freshwater Ecosystems, 7, 105117.
  • Williams, D.D. (2006) The Biology of Temporary Waters. Oxford University Press, Oxford, UK.
  • Williams, P., Biggs, J., Fox, G., Nicolet, P. & Whitfield, M. (2001) History, origins and importance of temporary ponds. European Temporary Ponds: A Threatened Habitat (ed. by K. Rouen), pp. 715. Freshwater Biological Association, Birmingham.
  • Wissinger, S.A. (1999) Ecology of wetland invertebrates. Synthesis and applications for conservation and management. Invertebrates in Freshwater Wetlands of North America (ed. by D. Batzer, R.B. Rader and S.A. Wissinger), pp. 10431086. Wiley, New York City, New York.
  • Zacharias, I., Dimitrou, E., Dekker, A. & Dorsman, E. (2007) Overview of temporary ponds in the Mediterranean region: threats, management and conservation issues. Journal of Environmental Biology, 28, 19.

Appendices

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Appendices

Appendix 1. List showing the taxa captured in the study ponds (A is for adults, L is for larvae, N is for nymphs). The percent occurrence across all ponds (total), as well as in temporary ponds, zacallones, caños, and the semi-permanent pond is provided. Percentages for ponds located in the northern and southern areas of the park are given separately

TaxaFamilyTotalTemporary Zacallones Caños Semi-permanentNorthern parkSouthern park
ALALALALALALAL
  1. a

    Confirmation of the identity of these species is required because species identification keys based on larval morphology are difficult to use and these species have not been previously cited as being in the study area.

  2. b

    Only occasional presence of this species was detected.

Acari
Hydrachnellae3 0 7 0 0 2 6 
Bassomatophora
Physa spp.Physidae33 28 30 67 100 37 17 
PlanorbidaePlanorbidae14 9 19 17 100 8 33 
Neotaenioglossa
Potamopyrgus antipodarum (Gray 1843)Hydrobiidae3 0 7 0 0 0 11 
Coleoptera
Bagous spp.Curculionidae16   7 0 100 21 0 
Dryops spp.Dryopidae341392300001000402110
Agabus conspersus (Marsham 1802)Dytiscidae39   59 17 100 34 56 
Agabus didymus (Olivier, 1795)Dytiscidae13 11 19 0 0 6 33 
Agabus nebulosus (Forster, 1771)Dytiscidae21   22 33 100 24 11 
Agabus spp.Dytiscidae 10 13 4 0 100 13 0
Cybister lateralimarginalis (De Geer, 1774)Dytiscidae3242264190170100224622
Dytiscus circumflexus Fabricius, 1801Dytiscidae4941147000031066
Eretes griseus (Fabricius, 1781)Dytiscidae3 4 0 0 0 3 0 
Graptodytes flavipes (Olivier, 1795)Dytiscidae3 2 4 0 0 2 6 
Hydaticus leander (Rossi, 1790)Dytiscidae 3 4 0 0 0 3 0
Hydroglyphus geminus (Fabricius, 1792)Dytiscidae33 24 48 17 100 23 67 
Hydroporus gyllenhali Schiödte, 1841Dytiscidae10 13 4 17 0 13 0 
Hydroporus lucasi Reiche, 1866Dytiscidae45 46 48 17 100 45 44 
Hygrotus confluens (Fabricius, 1787)Dytiscidae26 13 56 0 0 11 78 
Hygrotus inaequalis (Fabricius, 1777)Dytiscidae3 0 7 0 0 0 11 
Hygrotus lagari (Fery, 1992)Dytiscidae41 30 59 50 0 32 72 
Hydroporus spp. or Hygrotus spp.Dytiscidae 21 26 15 0 100 19 28
Hyphydrus aubei Ganglbauer, 1892Dytiscidae2020131333371700013154439
Ilybius montanus (Stephens, 1828)/Agabus bipustulatus (Linnaeus, 1767)Dytiscidae14 11 22 0 0 8 33 
Laccophilus minutus (Linnaeus, 1758)Dytiscidae3551335041563333010034503956
Liopterus atriceps (Sharp, 1882)Dytiscidae10 11 4 33 0 13 0 
Rhantus hispanicus Sharp, 1882Dytiscidae36 39 30 33 100 44 11 
Rhantus suturalis (McLeay, 1825)Dytiscidae16 15 22 0 0 15 22 
Colymbetes fuscus (Linnaeus, 1758)Dytiscidae41 39 52 17 0 37 56 
Rhantus spp. or Colymbetes fuscusDytiscidae 20 30 4 0 100 26 0
Yola bicarinata (Latreille, 1804)Dytiscidae6 0 19 0 0 0 28 
Gyrinus dejeani Brullé, 1832Gyrinidae19 15 30 0 0 11 44 
Gyrinus urinator Illiger, 1807Gyrinidae3 2 4 0 0 2 6 
Gyrinus spp.Gyrinidae 3 4 0 0 0 3 0
Haliplus andalusicus Wehncke, 1874Haliplidae6 7 7 0 0 3 17 
Haliplus guttatus Aubé, 1836Haliplidae5 2 11 0 0 3 11 
Haliplus lineatocollis (Marsham, 1802)Haliplidae5 2 11 0 0 3 11 
Haliplus spp.Haliplidae 6 9 4 0 0 6 6
Helophorus spp.Helophoridae45 46 41 50 100 48 33 
Limnebius furcatus Baudi, 1872Hydraenidae1 0 4 0 0 0 6 
Ochthebius dilatatus Stephens, 1829Hydraenidae3 4 0 0 0 3 0 
Ochthebius auropallens Fairmaire, 1879Hydraenidae5 7 4 0 0 3 11 
Hydrochus flavipennis Küster, 1852Hydrochidae4 2 7 0 0 3 6 
Anacaena lutescens (Stephens, 1829)Hydrophilidae54 61 37 67 100 60 33 
Berosus affinis Brullé, 1835Hydrophilidae21 22 22 17 0 19 28 
Berosus guttalis Rey, 1883Hydrophilidae15 17 11 17 0 18 6 
Berosus signaticollis (Charpentier, 1825)Hydrophilidae16 24 7 0 0 19 6 
Berosus spp.Hydrophilidae 26 35 11 17 100 32 6
Enochrus bicolor (Fabricius, 1792)Hydrophilidae23 22 26 0 100 21 28 
Enochrus fuscipennis (C.G. Thomsom, 1884)Hydrophilidae40 46 33 33 0 45 22 
Enochrus spp.Hydrophilidae 8 13 0 0 0 10 0
Helochares lividus (Forster, 1771)Hydrophilidae11 9 11 33 0 8 22 
Hydrobius convexus Brullé, 1835Hydrophilidae b   b            
Hydrobius fuscipes (Linnaeus, 1758) & Limnoxenus niger (Zschach, 1788)Hydrophilidae41 52 19 50 100 48 17 
Hydrobius spp. or Limnoxenus nigerHydrophilidae 28 37 19 0 0 35 0
Hydrochara flavipes (Steven, 1808)Hydrophilidae53440017010006300
Hydrophilus pistaceus (Laporte, 1840)Hydrophilidae 4 0 7 17 0 5 0
Laccobius revelierei Perris, 1864Hydrophilidae1 2 0 0 0 2 0 
Paracymus scutellaris (Rosenhauer, 1856)Hydrophilidae20 24 11 33 0 26 0 
Hygrobia hermanni (Fabricius, 1775)Paelobiidae3419242456151700023217211
Noterus laevis Sturm, 1834Noteridae8 4 7 17 100 8 6 
Hydrocyphon spp.Scirtidae 3 4 0 0 0 3 0
Ephemeroptera
Cloeon spp.Baetidae 74 76 81 17 100 69 89
Caenis spp.Caenidae 1 0 0 17 0 2 0
Haplotaxida               
TubificidaeTubificidae1 2 0 0 0 2 0 
Heteroptera
Corixa affinis Leach, 1817Corixidae81 78 93 50 100 77 94 
Micronecta scholzi (Fieber, 1860)Corixidae8 2 19 0 0 2 28 
Sigara lateralis (Leach, 1817)Corixidae45 39 63 17 0 35 78 
Sigara scripta (Rambur, 1840)Corixidae14 9 26 0 0 6 39 
Sigara selecta (Fieber, 1848)Corixidae3 2 4 0 0 2 6 
Sigara stagnalis (Leach, 1817)Corixidae14 9 26 0 0 6 39 
Trichocorixa verticalis (Fieber, 1851)Corixidae15 17 15 0 0 13 22 
Corixidae spp.Corixidae 56 59 59 17 100 53 67
Gerris cf maculatus Tamanini, 1946Gerridae10 11 7 17 0 11 6 
Gerris thoracicus Schummel, 1832Gerridae65 72 52 67 100 69 50 
Gerris spp.Gerridae 59 74 33 50 100 69 22
Microvelia pygmaea (Dufour, 1833)Microveliidae4 7 0 0 0 5 0 
Naucoris maculatus Fabricius, 1798Naucoridae9104919150000661722
Nepa cinerea Linnaeus, 1798Nepidae101094715333300118617

Anisops sardeus

Herrich-Schäffer, 1849

Notonectidae81 78 93 50 100 79 89 
Notonecta glauca Linnaeus, 1758 ssp. glaucaNotonectidae23 28 15 0 100 24 17 
Notonecta glauca Linnaeus, 1758 ssp. meridionalis Poisson, 1926Notonectidae29 28 33 17 0 29 28 
Notonecta maculata Fabricius, 1794Notonectidae14 13 19 0 0 13 17 
Notonecta viridis Delcourt, 1909Notonectidae26 20 41 17 0 26 28 
Notonectidae spp.Notonectidae 75 83 70 33 100 77 67
Plea minutissima Leach, 1817Pleidae3310241152110010002984417
Notostraca
Triops mauritanicus (Ghigi, 1921)Triopsidae3 4 0 0 0 3 0 
Spinicaudata
Cyzicus grubei Simon, 1886Cyzicidae1 2 0 0 0 0 6 
Maghrebestheria maroccana Thiéry, 1988Leptestheriidae3 4 0 0 0 3 0 
Anostraca
Branchipus cortesi Alonso y Jaume, 1991Branchipodidae1 2 0 0 0 2 0 
Branchipus schafferi Fischer de Waldheim, 1834Branchipodidae1 2 0 0 0 2 0 
Tanymastix stagnalis (Linnaeus, 1758)Tanymastigiidae1 2 0 0 0 2 0 
Streptocephalus torvicornis (Waga, 1842)Chirocephalidae3 4 0 0 0 2 6 
Odonata
Aeshna affinisa Vander Linden, 1823Aeshnidae 8 11 4 0 0 8 6
Aeshna mixta Latreille, 1805Aeshnidae 8 4 11 17 0 8 6
Coenagrion scitulum (Rambur, 1842)Coenagrionidae 6 7 7 0 0 8 0
Ischnura elegansa (Vander Linden, 1820)Coenagrionidae 28 30 26 0 100 31 17
Ischnura pumilioa (Charp., 1825)Coenagrionidae 38 41 30 33 100 44 17
Lestes barbarus (Fabr., 1798)Lestidae 13 15 11 0 0 16 0
Lestes dryas Kirby, 1890Lestidae 10 11 11 0 0 11 6
Lestes macrostigma (Eversm., 1836)Lestidae 1 2 0 0 0 2 0
Lestes virens (Charpentier, 1825)Lestidae 9 2 22 0 0 3 28
Crocothemis erythraea (Brullé, 1832)Libellulidae 4 4 4 0 0 3 6
Sympetrum fonscolombei (Selys, 1841)Libellulidae 51 67 26 33 100 63 11
Sympetrum meridionale (Selys, 1841)Libellulidae 19 33 0 0 0 24 0
Sympetrum sanguineum (Müller, 1764)Libellulidae 24 37 7 0 0 29 6
Sympetrum striolatum (Charpentier, 1840)Libellulidae 26 37 7 33 0 34 0
Orthetrum brunneuma (Fonscolombe, 1837)Libellulidae 1 0 4 0 0 0 6
Orthetrum cancellatum (Linneo, 1758)Libellulidae 1 0 4 0 0 0 6
Orthetrum nitidinervea (Selys, 1841)Libellulidae 1 0 4 0 0 2 0
  LNLNLNLNLNLNLN
Diptera
Chaoborus spp.Chaoboridae6 11 0 0 0 8   
Chironomus plumosus (Linneo, 1758)Chironomidae60 57 70 33 100 60 61 
CulicidaeCulicidae29164322711170003518611
Dixa spp.Dixidae5 9 0 0 0 6 0 
DolichopodidaeDolichopodidae1 2 0 0 0 2   
EphydridaeEphydridae 3 2 4 0 0 0 11
OrthocladiinaeChironomidae11 11 11 17 0 11 11 
RhagionidaeRhagionidae5 9 0 0 0 6 0 
SciomyzidaeSciomyzidae1 2 0 0 0 2 0 
TabanidaeTabanidae3 4 0 0 0 3 0 
TanypodinaeChironomidae9 7 7 33 0 8 11 
TipulidaeTipulidae5 9 0 0 0 6 0 

Appendix 2. Mean and standard deviation (SD) are shown for the pond characteristics (environmental and marsh-coast distance variables) of sampled ponds

EnvironmentalTemporary (= 46)Zacallones (N = 27)SemipCaños (N = 6)Transf.rs > 0.6a
MeanSDMeanSD(N = 1)MeanSD
  1. Temporary, temporary ponds; Zacallones, artificially deepened ponds; Semip, the semi-permanent pond; Caños, ponds filled by the running water of intermittent streams which mainly flow towards the marsh after heavy rains (N = number of ponds); Transf., independent transformations to approximate normality (Sqr is square root transformation; Log is Log(X + 1) transformation; _, is no transformation required); Rplant, plant richness; Max depth, maximum water depth; Pond area, pond surface area; Pond number, total number of ponds with an extension >150 m2 into a 200 m buffer area around each pond; Flooded area, total flooded surface area in a 200 m buffer area around each pond; i–P, dissolved inorganic phosphate; TP-s, sediment total P; TFe-s, total Fe concentration in the sediment; O2, dissolved oxygen concentration; EC, electrical conductivity; O.M., organic matter; Dmarsh, the minimum linear distances from each pond to the border of the marsh; Dcoast, the minimum linear distances from each pond to the coast.

  2. a

    Significant Spearman coefficient higher than 0.6 (all P < 0.01) indicating variables with positive (+) and negative (−) correlations.

Rplant7.43.73.93.0146.23.5_Pond area (+)
Max depth (cm)59.831.8103.813.211852.234.2_ 
Pond area (m2)33905586119329122672538408LogAq plant R (+)
Pond number5.14.03.13.4112.82.2_Flooded area (+)
Flooded area (m2)381462681649339763 28212271993LogPond number (+)
NH4+(mg l−1)0.170.540.120.150.050.140.12Log 
i–P (mg l−1)0.110.130.080.100.010.210.28Log 
TP-s (μg g−1 d.w.)258.36314.59105.29121.80162.56328.40333.52_TFe-s (+), O.M. (+)
TFe-s (mg g−1 d.w.)3.303.182.362.617.614.574.41_TP-s (+), O.M. (+)
O2 (mg l−1)2.01.82.53.62.83.63.1Log 
pH6.50.77.71.16.96.70.6LogEC (+), Alk (+), Na+ (+), Ca2+ (+), K+ (+), Mg2+ (+)
EC (µS cm−1)670.81270.61304.71226.41224479.2356.3LogpH (+), Alk (+), Cl−1 (+), Na+ (+), Ca2+ (+), K+ (+), Mg2+ (+), coast (−)
Alkalinity (meq l−1)1.992.724.964.221.471.570.82LogpH (+), EC (+), Cl−1 (+), Na+ (+), Ca2+ (+), K+ (+), Mg2+ (+)
Turbidity (NTU)275117183192385Log 
O.M. (%)7.026.602.633.493.936.086.00_TP-s (+) and TFe-s (+)
Cl−1 (meq l−1)4.207.818.6511.834.243.792.91LogEC (+), Alk (+), Na+ (+), Ca2+ (+), K+ (+), Mg2+ (+)
Na+ (meq l−1)3.886.477.287.874.33.192.41LogEC (+), Alk (+), Cl−1 (+), Ca2+ (+), K+ (+), Mg2+ (+)
Ca2+ (meq l−1)0.851.152.192.410.590.670.47LogEC (+), Alk (+), pH (+), Cl−1 (+), Na+ (+), K+ (+), Mg2+ (+)
K+ (meq l−1)0.160.320.330.380.030.100.06LogEC (+), Alk (+), pH (+), Cl−1 (+), Na+ (+), Ca2+ (+), Mg2+ (+)
Mg2+ (meq l−1)0.921.343.343.861.050.710.63LogEC (+), Alk (+), pH (+), Cl−1 (+), Na+ (+), Ca2+ (+), K+ (+), Mg2+ (+)
Na+/Ca2+ ratio14.4958.084.924.807.244.931.24Log 
Marsh-coast distance
Dmarsh (m)22931798218014503519234410_ 
Dcoast (m)5097252930852099216895232806 EC (−)